Training Hiring AI Not to be Biased

Artificial Intelligence (AI) and Machine Learning (ML) play integral roles in our lives.  In fact, many of you probably came across this blog post due to a type of one of these systems.  AI is the idea that machines should be taught to do tasks (everything from search engines to driving cars).  ML is an application of AI where machines get to learn for themselves based on available data.

ML is gaining popularity in the evaluation of job candidates because, given large enough datasets, the process can find small, but predictive, bits of data and maximize their use.  This idea of letting the data guide decisions is not new.  I/O psychologists used this kind of process when developing work/life inventories (biodata) and examining response patterns of test items (item response theory—IRT).  The approaches have their advantages (being atheoretical, they are free from pre-conceptions) and problems (the number of people participating need to be very large so that results are not subject to peculiarities about the sample).  ML accelerated the ideas behind both biodata and IRT, which I think has led to solutions that don’t generalize well.  But, that’s for another blog post.

What is important here is the data made available and whether that data is biased.  For instance, if your hiring algorithm includes zipcodes or a classification of college/university attended, it has race baked in.  This article has several examples of how ML systems get well trained on only the data that goes in, leading to all kinds of biases (and not just human ones).  So, if your company wants to avoid bias based on race, sex, and age, it needs to dig into each element the ML is looking at to see if it is a proxy for something else (for instance, many hobbies are sex specific).  You then have to ask yourself whether the predictive value of that bit is worth the bias it has.

Systemic bias in hiring is insidious and we need to hunt it down.  It is not enough to say, “We have a data driven system” and presume that it is not discriminatory.  If the ML driving it was based on inadvertent bias, it will perpetuate it.  We need to check the elements that go into these systems to ensure that they are valid and fair to candidates.

I’d like to thank Dennis Adsit for recommending the article from The Economist to me.

Adjusting Your HR Strategy When Your Company Decides to Train For Basic Job Skills

There is a presumption that the US education system will provide employers with workers that possess requisite job skills.  Companies are then responsible for providing more advanced ones through apprenticeships, job training, and leadership development.  But, what if job seekers do not possess the skills for tech jobs?

This article describes what lengths some employers are going to get people in their talent pipeline.  In many ways, there is nothing new here.  It comes down to searching for talent where they previously hadn’t and providing training rather than expecting people to come with skills.  It’s the latter that I find most interesting.

When designing selection programs, particularly for entry level positions, we tend to focus on what knowledge or skills the candidates needs on the first day.  Those expectations are higher if we expect someone to come with experience than if we are going to be providing a lot of training.  This has important impacts on how we select candidates, including:

  1. Use of aptitude tests rather than knowledge tests.  Aptitude tests are terrific measures of basic skills and are quite valid.  However, speeded ones can lead to adverse impact, so they require good validation studies, meaningful passing scores, and adverse impact analyses.
  2. Alter interview questions so that a wide variety of experiences can be used to answer them.  If you are hiring people who don’t have experiences in your industry, you should be asking valid questions that people with little or no job experience can answer.  For instance, instead of, “Tell me about a time when you led a team project at work and…” use “Tell me about a time when you had to influence a group of friends and…”
  3. Focus on reducing turnover.  Training is EXPENSIVE, so hiring mistakes in a boot camp environment are very costly.  Take special care in developing realistic job previews and other ways that allow candidates to decide if they are not a good fit.  Collect information (previous experiences, referral sources, school majors, etc.) that may be indicative of future turnover and validate them.  These can be part of very useful pre-employment processes.

What this approach really presents is a change in HR strategy from one that relies on people to be able to start on day one to taking time to get them up to speed.  By having recruitment, selection, and development leaders involved in the execution, organizations can adapt their tactics for identifying and selecting talent and have a smoother transition.

How Often Should You Use Your Gut Instinct? How About Never?

Why do tests predict job performance better than interviews?  Because interviewers let their “gut instinct” cloud their judgment and introduce lots of related bias. 

This recent article suggests (without any data to back it up) that sometimes we should just trust our gut because it is better at predicting the future than our analytical mind, which is better at predicting the past.  Huh?  Our instant reactions to something make us psychic?

In Nobel Prize winner Daniel Kahneman’s book Thinking, Fast and Slow he summarizes decades of research on decision making.  He describes our fast, “gut instinct” thinking as System 1.  Let’s talk about a few of the reasons why this kind of decision making leads us to poorer decisions:

  1. System 1 thinking is highly influence by irrelevant numbers.  For instance, valuing something at a higher price if the first cost is presented at $50,000 than if the first cost is presented at $25,000.

  2. This level of thinking leads us to make judgments based on how easily we can think of examples.  When we can think of those instances, we give them higher probabilities of occurring.

  3. Our gut is overconfident—it assumes we have more control than we do.  Kahneman explains that System 1 decision making involves only our own experiences, which are a small and does not account for randomness.  Despite the article above saying that our gut instincts are forward thinking, it is just the opposite.  System 1 thinking assumes that what I experienced before is a far greater predictor of the future than it is.

If your instinct tells you that an upcoming decision is wrong, don’t just trust it.  Do some research and/or talk to others and see if you are falling into a System 1 pitfall. 

We rarely have 100% of the data we want before making business decisions.  But, throwing away what we have because going in another direction “feels” better is not a recipe for success. 

Let’s put this in a selection context.  Our gut tells us that people who are similar to ourselves in background and experience are the best hires.  Slower thinking tells us to look at other factors, such as skills and abilities before making such decisions.  And when we do so, we make better hiring choices.

Going with your gut instinct It may sound sexy and empowering, but it is not effective.  Our slower System 2 (per Kahneman) processing system, despite its own set of biases, is more likely to lead us in the right direction.

Are we Biased AGAINST Top Talent?

We all want to believe that we are looking to recruit, select, and develop top talent.  We spend lots of time reading and writing articles on the topic.  But, what if hiring managers are not interested?

This article throws a bit of cold water on the topic.  It documents a study where hiring managers were shown to doubt the organizational commitment of those deemed the most capable.  It was almost as if they were saying, “Why would someone really good want to work for us?”

There are several issues at work here.  But, what they boil down to is a bias among hiring mangers that negatively affects their selection processes.  Sure, I can imagine anecdotal evidence (“Yeah, we hired that one really bright person, but she jumped ship as soon as she got a better offer.”), but I don’t think that this is a data driven decision.

What this also underlines is the importance of developing a culture that encourages top talent to stay.  There’s no question that selecting the right people will drive business performance.  And having a culture that acknowledges and rewards high performance will do so as well.  When hiring managers feel that top talent will not stay, it is really an indictment of the culture rather than an accurate prediction of management’s view.  How can you fight this?

  1. If managers do not think top talent would be committed to your organization, they should NOT be involved in hiring. 
  2. Those who are doing the hiring should be able to provide a realistic preview of the organization, but should also be able to succinctly describe why people stay.  And I’m not just talking about a good cafeteria.  They should be able to provide examples of people who have found challenging work over time in the organization.
  3. If you are speaking with hiring managers who show an anti-talent bias, ask them what needs to be changed so they would believe that top talent would want to stay.
  4. The best way to fight bias is with data.  You should be able to study turnover rates by talent bands (contact me for tips on this).  This way you can either show people that top talent does not leave any faster than other employee groups or demonstrate to executives that this is a problem that needs to be addressed.

Organizations should strive for selection processes that identify top talent and cultures that nurture them.  Do not let bias against hiring top talent work against these two initiatives.

Shaping Skills to Your Work

It is important to use valid selection tools to hire people for the work you have for them.  But, what happens when technology changes the tasks or the jobs get replaced by automation?  You can let people go as their work becomes obsolete and hire new staff.  However, in times with low unemployment, this strategy will be difficult to execute.  Or, you can train people to acquire the new skills.  This has big implications in industries where tech is changing the nature of work, such as mining and warehousing, just to name two.  However, trying to train lots of people in new skills assumes that they have the interest and aptitude for learning them.  Remember, people chose to pursue their given job/career for a reason.

Amazon and Walmart provide an example of this type of investment.  Their programs include technical and college training.  What is telling about their plans is that neither company considers it a “nice to have.”  Rather, it is an acknowledgement that the skills it takes to run their businesses are changing and they don’t think they can find enough talent to meet future needs in the labor pool.  This may be because so many young people want a career that requires as little work as possible.

The situation also makes one think about selecting people for industries where the skills required change rapidly.  Instead of using tests or interviews that focus on specific abilities, perhaps addressing broader ones, such as openness to new experiences and general aptitude, will serve companies better.

Drilling for Good Candidates

The current low unemployment rates and data mining have led to companies tossing out wider and wider nets to fill positions.  But, is all of this confusing activity with productivity?

This article (thanks to Dennis Adsit for bringing it to my attention) brings up some great reminders about some very solid things that employers should be doing (valid testing, structured interviews, etc.) and avoiding others (tech is NOT a magic bullet for recruitment and selection).  However, it also does a good job of challenging some basic assumptions about hiring, all of which can be evaluated.  These include:

  • Unless you are adding positions, why are you looking for so many outside candidates? One reason people leave companies is because they do not feel they have promotional opportunities.  One reason you are looking for so many outside candidates is that people quit.  Chicken, meet egg.
  • Taken a step further, HR really needs to test the effectiveness of its processes on an ongoing basis. If there is data to support that, in general, outside candidates perform better than those promoted, then keep on searching for them.  And you should probably revamp your entry level recruitment and hiring processes.  If not, then career development and taking steps to increase internal mobility will be more effective actions than scouring the universe of passive candidates for new hires.
  • Develop measures to evaluate the success of what you are doing. Few things frustrate me more than a client saying, “We cannot measure someone’s individual performance.”  Really? Does that mean the cost of turnover is the only reason you keep people in their jobs? Granted, it can take some time to measure output, but you can typically find ways of evaluating a person’s contribution to a team.  If a manager says, “I like/don’t think this person is effective” she should be able to say why.
  • Related to the above, don’t assume that a good process will always have the same effectiveness. As your business changes, recruiting and selection systems need to adapt as well.

I do not think that HR has to constantly be reinvented.  But, basic assumptions should occasionally be challenged.  It is only by measuring and evaluating our processes that we can truly improve them.

Putting Too Fine a Point On It

I will admit that I am more of a big picture person than a perfectionist.  Going through old blog posts would likely lead to the finding of some spelling errors and grammatical mistakes.  That does not bother me as long as I am getting my point across.  I also have a pretty good sense that I am in the minority of people who are willing to admit that I lack a big attention to detail.

At the same time, I also advise clients to use pre-employment tests that measure attention to detail and conscientiousness for those jobs that require it.  So, like other personality traits, it certainly has its place.  I’m just not the person you want looking for needles in haystacks.

So, this article definitely caught my attention.  Here’s the most important takeaway (at least to me) from the authors, “…the answer to the question ‘is perfect good?’ is that in total, perfectionism is likely not constructive at work.”  Given this, what are we really getting when a job candidate tells us that he is a perfectionist?

The data shows that we will get someone who will work long hours, but is not more likely to be engaged in the work.  Rather, perfectionists tend to burn out more than those who can let the little things slide.  This is particularly true of those whose perfectionism comes from a place of avoiding failure than those striving to be excellent.

Most importantly, when compared to supervisor ratings of job performance, y’know, the people the perfectionist is trying to impress, levels of perfectionism are not related.  That’s right—managers feel that the job performance of those who feel that good is good enough is the same as for those who choose to gild the lily.

From a selection perspective, a more subtle approach is called for.  There are some jobs (brain surgeon and quality inspector, to name two) where perfectionism is probably important and should be part of your assessment process.  However, it should not be considered a good universal predictor of performance.  One can easily imagine some jobs where being a perfectionist would be a negative predictor, such as creative jobs like marketing or app design. Also, when interviewing, if a candidate brags about her perfectionism, I would not get too excited.  She may be confusing activity for productivity.

The organizational implications here are straightforward: Having a culture of perfectionism will get you more hours, but not better performance, out of your team.  While not explicitly tested in the article, it is likely to also get you more turnover.  This is a reminder that we should be clear about quality expectations and work-life balance.

Selecting Managers Who Understand the Value of Praise

When I do leadership/management workshops, the first topic is always motivation.  While I am a big believer that motivation must come from within, managers can impact performance, in the short term, by effectively using rewards.

Years of research tells us that cash and other extrinsic rewards can be effective motivators for tasks where individual effort leads to individual results.  However, the bigger the distance between effort and results, the less value these incentives have.  Oh, and they also lose their effect over time.

The wise manager knows that recognition, praise, and other behaviors that lead to intrinsic rewards are much more powerful. This article provides a good synopsis on how to use a combination of intrinsic and extrinsic rewards.

While there tends to be a strong focus on rewards, something that gets overlooked is how to select managers who already have this insight.  Sure, most can learn it. But, I would think that there are traits that predict how well a person rewards employees.  Three of these would include:

  • A person with a high level of agreeableness is usually warm, friendly, and tactful. They generally have an optimistic view of human nature and get along well with others.  People high on this trait are likely to want to make others feel engaged in their work.
  • Generous people are the ones who give more than is expected of them.  Giving a reward to another person is an act that provides praise or a reward to another person when it could be kept to oneself.
  • View of Employees. Managers who have a “your paycheck is your reward” mentality are not likely to give out a lot of praise.  Those who recognize people as individuals, and learn what their needs are, will be much more likely to provide meaningful motivators.

By making motivational skills part of the valid selection process, we are more likely to hire managers who will seek out opportunities to reward results.  Appropriate use of such techniques will lead to more engaged and productive employees.  They are less likely to turnover, which is critical in our current low unemployment economy.

Celebrating (Painful) Learning

In a previous post I talked about using the Marshmallow Challenge to provide insight into cultures that support risk taking.  Taking the stigma out of making mistakes is one way to encourage creativity.

Taking this to the next level are FUN nights (note that curse words figure prominently into the article).  This is where entrepreneurs are encouraged to share their failures with others.  The thought is that the process makes people more relatable than if they only share your successes.  The promoters feel this leads to better networking among the members.

Organizations could adopt this approach as well, but it would take a bit of a balancing act.  Most companies want their executives to be approachable, but also want them seen as competent.  Employees want to avoid being branded as “the person who had the bad experience.”

The key is to not just share stories of failure.  Rather, talk about growth. When executives reveal experiences about what they learned from mistakes, others can see that risk taking, and the inevitable missteps that come with it, are part of the process of becoming successful.

From a selection perspective, there are traits you can look for in hiring potential leaders who are pre-disposed to this kind of learning.  One is openness to experience.  The other is self-confidence.  Validating these types of measures will help you hire people who are willing to confront their mistakes and share their lessons with others.

Can we Predict Karma in Applicants?

We would all like to think that doing well towards others will benefit us in this (and future?) life.  At the same time, capitalism can encourage some people to act entirely in their self-interest in order to get ahead and create efficient organizations.  So, should we hire good or successful people?

Altruism is a part of the “Big 5” personality construct of Agreeableness.  In business settings, we would consider it how much a person makes other feel welcome versus looking down on others.  Agreeableness has been established as a reasonably good predictor of job performance.

In this study, researchers dug into altruism and how it affected life outcomes, including income.  There are two things that I found interesting and useful about their results:

  • We should not be surprised that altruism leads to financial success in environments that involve teamwork. Working with others is about making 2+2=5 and people need to be willing to think about others to make that happen.  It is important to note that this research did not look at specific occupations.  One can see how altruism would be a bigger plus for some professions (health care) but not in others (sales).
  • Generally, the results showed a straight-line relationship between altruistic motivations and income. However, when looking at altruistic behaviors, there was a point where there could be too much of a good thing an income went down among those who reported the most altruistic behaviors.  This is important from a selection perspective because if you were to use a personality measure (Compared to most people I know, I am very altruistic.), you would want to score it as higher is better.  However, if using a biodata approach (How many times have you given your time to someone else in the past year?), then a curvilinear scoring may be more accurate.

Of course, deciding on any pre-employment screen first requires a good job analysis first.  This study provides an interesting window into how one sub-facet of personality can potentially be predictive of important job behaviors.

It also reminds us to look at the value of pro-social behaviors in the workplace and look for opportunities for employees to do them together.  It builds a culture of altruism that may also lead to greater business success.

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